This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. Traditional methods for the analysis of human functional neuroimaging data (e.g., fMRI) focus on isolated brain regions (e.g., by using General Linear Model) or a small set of brain regions (e.g., by using Structural Equation Modeling). While informative, these methods fall short of informing us about the interactive nature of regions across the entire brain. In this pilot study, we aim at examining large-scale human brain networks by using methods that are conducive to such analysis (e.g., methods that are used in conjunction with graph theory). Typically, 30,000 regions (in the form of voxels) are collected from one subject at one time point of the experiment (a typical experiment consists of hundreds of time points). The high computational demands make the analysis extremely difficult in our current facility. We seek to use the super-computing resources provided by the CyberInfrastructure Partnership to perform such large-scale analyses on our data collected using NIH funds.